1. Field of the Invention
The present invention relates to medical imaging and analysis. More particularly, the present invention relates to methods and apparatuses for detecting lesions, abnormalities or other anatomical structures in medical images.
2. Background Art
Medical imaging is generally recognized as key to better diagnosis and patient care. It has experienced explosive growth over the last few years due to imaging modalities such as x-ray, Computed Tomography (CT), ultrasound, Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Conventionally, medical images have been inspected visually by highly-trained medical practitioners in order to identify anatomic structures of interest, such as lesions. However, the process can be tedious, time consuming and must be performed with great care.
Computed Tomography Colonography (CTC) is a form of medical imaging that has been used for the detection and screening of colonic cancers. The purpose of CTC is to locate and identify polyps on the colon wall by computationally processing CT scans. A polyp is an abnormal growth on a mucous membrane, and may be benign or malignant (cancerous). The effectiveness of CTC is hindered by the large amount of data generated by a CTC scan, variability among human experts and the complexity of the classification. A typical modern CTC scan produces around one thousand axial CT images (slices) for the supine and prone data sets. To address these problems, a number of Computer Aided Detection (CAD) schemes for detecting polyps with CTC have been proposed.
Existing CAD algorithms detect polyps through their shapes by estimating curvature, either directly or indirectly, and define features based on these curvatures and use them in the detection and classification phases. For example, Yoshida et al. [1, 2] characterized several features including the shape index, curvedness, gradient concentration and directional gradient concentration from a conventional label segmentation of the colon wall to try to distinguish polyps from the normal colon tissues. However, Yoshida et al.'s method can sometimes fail to distinguish polyps from the colon wall adequately.
Thus, there is a need for improved methods for computer aided detection of lesions, abnormalities or other anatomical structures in medical images. In particular, there is a need for an improved automated system for detecting and classifying polyps, especially for use in Computed Tomography Colonography, that is able to distinguish polyps from the surrounding tissue more reliably.